Recent studies have significantly enhanced the performance of single-image
super-resolution (SR) using convolutional neural networks (CNNs). While there
can be many high-resolution (HR) solutions for a given input, most existing
CNN-based methods do not explore alternative solutions during the inference. A
typical approach to obtaining alternative SR results is to train multiple SR
models with different loss weightings and exploit the combination of these
models. Instead of using multiple models, we present a more efficient method to
train a single adjustable SR model on various combinations of losses by taking
advantage of multi-task learning. Specifically, we optimize an SR model with a
conditional objective during training, where the objective is a weighted sum of
multiple perceptual losses at different feature levels. The weights vary
according to given conditions, and the set of weights is defined as a style
controller. Also, we present an architecture appropriate for this training
scheme, which is the Residual-in-Residual Dense Block equipped with spatial
feature transformation layers. At the inference phase, our trained model can
generate locally different outputs conditioned on the style control map.
Extensive experiments show that the proposed SR model produces various
desirable reconstructions without artifacts and yields comparable quantitative
performance to state-of-the-art SR methods.

本文提出了一种基于多任务学习和条件性目标实现调节损失权值的单个可调节超分辨率模型，通过一组权重定义为样式控制器来优化超分辨率模型，并在 Residual-in-Residual Dense Block 上装备空间特征转换层来适应目标。实验表明，该模型能在条件控制下生成不同的超分辨率图像，并且输出质量优秀，性能优于现有的超分辨率方法。

使用有条件的目标函数进行灵活的风格图像超分辨率

Flexible Style Image Super-Resolution using Conditional Objective

The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work
that is capable of generating realistic textures during single image
super-resolution. However, the hallucinated details are often accompanied with
unpleasant artifacts. To further enhance the visual quality, we thoroughly
study three key components of SRGAN - network architecture, adversarial loss
and perceptual loss, and improve each of them to derive an Enhanced SRGAN
(ESRGAN). In particular, we introduce the Residual-in-Residual Dense Block
(RRDB) without batch normalization as the basic network building unit.
Moreover, we borrow the idea from relativistic GAN to let the discriminator
predict relative realness instead of the absolute value. Finally, we improve
the perceptual loss by using the features before activation, which could
provide stronger supervision for brightness consistency and texture recovery.
Benefiting from these improvements, the proposed ESRGAN achieves consistently
better visual quality with more realistic and natural textures than SRGAN and
won the first place in the PIRM2018-SR Challenge. The code is available at
this https URL .

该研究通过提出 ESRGAN，改进了 SRGAN 的网络架构、对抗损失和感知损失三个关键部分。ESRGAN 获得了 PIRM2018-SR 挑战的第一名，并取得了比 SRGAN 更好的视觉质量和更真实、更自然的纹理。